Using Anaplan for Supply Chain Planning

We hear from many prospective customers about the challenges they face with accurately and efficiently conducting their supply chain planning. One area where we’ve gained a lot of experience is helping customers enhance their S&OP processes. Historically, these businesses would have relied on Microsoft Excel and Access solutions to complete their planning, but the reality of using these tools is often far from ideal. The sort of challenges they face are as follows:

• Capturing inputs from multiple users across the business is tricky. Spreadsheets end up being emailed around the organisation and then linked back together in a confusing web of linked spreadsheets.
• The level of granularity that can be achieved through a spreadsheet may not be enough for the business to plan accurately.
• Building in the ability to calculate a statistical forecast or seasonality can be tricky in Excel.
• Sharing the results of a forecast across the business can be slow, especially when different versions of the truth emerge. It can then be very time consuming to produce reporting packs which may end up needing to be redone if the plan gets updated.

Mentat Technology has worked with several customers to deliver S&OP solutions in Anaplan. We’ve helped these customers remove the limitations presented by their old solutions and realise the benefits of implementing a cloud-based, integrated model. In turn, these companies have gone on to roll-out Anaplan across other parts of their organisation such as Finance, thereby leveraging the benefits of connected planning.
Our customers have reported significant reductions in the time it takes to produce forecasts, and that has allowed them to increase their forecasting frequency. Some customers have chosen to implement statistical forecasts that greatly improve the accuracy of their sales forecast, to the point where the accuracies of these statistical forecasts beat those of their own sales team!

Read more about how Mentat Technology have helped Tarmac use Anaplan for their supply chain planning.
If you would like to learn more about how Mentat Technology can help with supply chain planning, please contact us.
Read more about Supply Chain planning in Anaplan 

Anaplan Best Practices – Large Data Volumes

How can we use Anaplan to reduce cell sparsity from 360 million, to only 5 million cells?

It is a very common scenario where our clients need to be able to load transaction level data into Anaplan. In most cases this is due to bottom-up calculations, external system restrictions or low-level allocations/mappings. Large volumes of data significantly impact Anaplan model size and can even affect the performance, therefore we would like to share a couple of design techniques that will make the model efficient.

Staging lists

The requirement to import transaction level data doesn’t mean such high level of details has to be permanently stored within the model. In the vast majority of cases the main driver for high volume data loads are low level allocations and mappings between multiple data sources, but after the initial processing the details are no longer required. In such a scenario, using a staging list approach is the best design solution.

Staging lists process:

1. The data is initially loaded (via user action or automated process) into the model
2. System validates the data and runs low level calculations
3. User reviews results and validations (optional backup export can be enabled at this stage as well)
4. User submits the results by running a process (dashboard button)
5. The system aggregates the data using predefined unique key to a level that provides all required attributes and assures optimal size and performance efficiency
6. Source data loaded in p1 is being removed from the model

The design provides high quality bottom-up results and eliminates ineffective workspace utilisation and performance loses.

Multi-level Numbered lists

Multi-dimensional data cubes are the default way in Anaplan to deliver required results, but in some cases these prove to be inefficient or even not possible – this is when numbered list hierarchies prove to be the best solution.
To illustrate the efficiency of the technique we will use an example of a Claims triangle report for the purpose of Solvency II reporting.
In order to build the report, we typically need the following details: Reserving Class, Policy, Risk Code, Year of Account, Development Period. The example volumes are as follows:
– Reserving Cl = 30 items
– Policies = 20,000
– Risk Codes = 80
– YoA = 15
– Dev Period = 15
If all the attribute lists were added to a module it would result in following size for each measure (Res Cl is removed from calculation as it’s a direct parent of policy):
20,000 x 80 x 15 x 15 = 360,000,000
Even this simplified and fairly low volume example results in significant report size.

The key for efficient design is to understand the data – in this example, it is logical that not all the policies will have values for every YoA or development period, let’s assume an average of 7 years/periods per policy, on top of that a single policy usually does not have more than 5 unique risk codes. Following numbered list hierarchy would be created:
– L1 Reserving Class
– L2 Policies
– L3 Risk Codes (unique combination of RCs applicable for each policy)
– L4 YoA (unique combination of valid Policy, RC and YoA)
– L5 Dev Period (unique combination of valid Policy, RC, YoA and Dev Periods)
The total size of a single measure using above mentioned assumptions would be:
30 (Res Cl) + 20,000 * 5 * 7 * 7 = 4,900,030
As we can see the numbered list approach is significantly more efficient and the results can easily be exported in a pivot-friendly format or sent to other external systems.

These two techniques prove to be very powerful when processing large volumes of data and can either be used individually or in conjunction to provide the best solution for our client.

Anaplan Partner Hub

This week we attended Anaplan’s first full day Partner Hub in London. It was a chance to learn more about the platform changes being planned for release in the short, near and long-term and to also hear about their vision for how they see the product evolving over the coming years.

There was too much covered during the day to share everything, but some of the highlights for us were the demonstrations we saw of the new workflow functionality and Excel add-in. Anaplan have clearly invested a lot of time and resources in redeveloping both of these – and the results are impressive.

The new Anaplan workflow, or ‘Tasks’ as we will probably come to know it, allows planning cycles to be much more task driven. Instead of accessing their planning dashboards within the models as they would have done previously, users can receive an email notification which will take them to a new Tasks portal. From here they can see all tasks assigned to themselves and complete each one in order – without leaving the portal.

The new Excel add-in currently in development has made huge improvements since the current release. One major change in the new build is that the add-in allows users to manage the data they retrieve themselves, rather than relying on a model builder to create the appropriate views within the model. As a result, users can be more selective about what data they pull in to Excel from Anaplan. We have also seen big improvements in how data can be pivoted and sliced once it is in Excel.

If you would like to understand more about upcoming changes to Anaplan, please contact us.